Nearly 80 years ago, in July 1945, MH Hasham Premji founded Western India Vegetable Products Limited in Amalner, a town in the Jalgaon district of Maharashtra, India, located on the banks of the Bori River. The company began as a manufacturer of cooking oils.
In the 1970s, the company pivoted to IT and changed its name to Wipro. Over the years, it has grown to become one of India’s biggest tech companies, with operations in 167 countries, nearly a quarter of a million employees, and revenue north of $10 billion. The company is led by executive chairman Rishad Premji (19459061) is the grandson of founder Rishad Premji.
Crin Minandram, VP and Cto of WIPRO Fullstride Cloud
Image: Wipro
Today, In this exclusive interview with ZDNET, Wiprodescribes themselves as a “leading global end-to-end IT transformation, consulting, and business process services provider.”
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As a leader in strategic technological initiatives, he leads the development of solutions that are future-oriented. His primary role is driving innovation and empowering organizations by providing them state-of-the art solutions.
With an emphasis on cloud computing, he designs and implements advanced cloud architectures to transform the way businesses operate. He also optimizes operations, enhances scalability and fosters flexibility in order to propel clients on their digital journeys.
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AI has become an important focus for the company. In this interview, I had the chance to discuss the importance and sustainability of AI ethics as it relates to the future of IT.
Letās dig in.
Values of the company
Q: What is ethical AI and why is it important for businesses today?
KIRAN Memory Armo () : Ethical artificial intelligence is not only in compliance with the law, but also aligned to the values we hold dear at Wipro. Our work is based on four pillars.
AI should be aligned to our values for the individual (privacy, dignity, fairness, transparency and human agency), society (fairness and transparency), and the environment. The fourth pillar, technical robustness, includes legal compliance, safety and robustness.
ZDNET: Why are many businesses struggling with AI ethics and what are the main risks they should be addressing?
: The lack of a common AI vocabulary is often the cause of the struggle. The first step is to create a cross-organizational approach that brings together legal, HR and technical teams. AI is a transformative technology and requires a corporate strategy. Second, organizations must understand the key tenets that will guide their AI approach. This goes beyond the laws and encompasses their values.
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Lastly, they can create a risk classification based on the threats they anticipate. Risks are based upon legal alignment, security and the impact on workforce.
How does AI adoption affect corporate sustainability goals both positively and negatively, ZDNET?
: AI adoption will have and will continue to have a significant effect on corporate sustainability goals. AI can improve operational efficiency through optimizing supply chains, improving resource management by monitoring energy and carbon consumption more precisely, and improving data collection processes to report regulatory requirements.
AI can be used to optimize transportation routes by manufacturing or logistic companies, resulting in reduced carbon emissions.
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On the other hand, rapid AI development and deployment leads to increased energy consumption, carbon emissions, and water usage. Training large AI models demands significant computational power, resulting in a larger carbon footprint.
Impact on the environment
Q: How can enterprises balance their drive for AI innovation and environmental responsibility?
: As a first step, enterprises should establish clear policies, guidelines, and principles on the sustainable usage of AI. This provides a baseline to help teams make decisions about AI innovation. It also allows them to make the best choices for the types of AI infrastructure, algorithms, and models they will use.
Enterprises should also establish systems that track, measure, monitor, and analyze the environmental impact of AI usage, and demand it from their service providers.
Our clients have helped us evaluate AI policies, engage stakeholders both internal and external, and develop new AI and environment principles before training and educating staff across multiple functions to embed thinking into everyday processes.
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By fostering greater transparency and accountability in companies, they can drive meaningful AI innovations while remaining conscious of their environmental commitments. A number of cross-industry, cross-stakeholders groups are being formed to help enterprises explore the environmental dilemmas, measurements requirements, and impact associated AI innovation.
In an incredibly fast-moving world, it is important to learn from others and collaborate on a global scale. Wipro has led global collaborative efforts on AI and environment alongside our customers. We are in a good position to help our customers navigate the regulatory landscape.
How are global regulations evolving in order to address ethical AI concerns and sustainability concerns?
KM : AI has never existed alone. AI is governed by privacy, consumer protection, human rights, and security legislation. Data protection regulators are crucial in protecting individuals from AI’s harms. Consumer protection is important when it comes algorithmic pricing. Non-discrimination laws can also be used to support discrimination based on algorithmic pricing.
Organizations must understand how current legislation applies to AI, and they should train their workforce in how to integrate legal protection, privacy and security into AI adoption.
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AI-specific legislation is being introduced in addition to existing laws. In Europe, the EU AI Act regulates the marketing of AI products. The more risky the product, the greater the need for controls.
In America, individual states are legislating AI, particularly in the contexts of labor management and AI deployment, which is arguably the most complex area of AI deployment.
The biggest misconception
According to ZDNET, what are the most common misconceptions about AI ethics? How can businesses overcome these misconceptions?
: The biggest myth is that it’s difficult to combine innovation and responsibility. Responsible AI is key to unlocking AI advancement as it offers long-term, sustainable innovation.
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In the end, consumers and businesses will choose products that they trust. Trust is therefore the cornerstone of AI deployment. Companies that combine innovation with trust will have a competitive advantage.
How does Wipro FullStride Cloud help companies align AI with ESG goals (environmental, Social, and Governance)?
: We begin by developing responsible AI frameworks to ensure fairness, accountability, and transparency within the AI models. We also use AI to track ESG metrics and report them, as well as Green AI projects such as tools that measure and reduce AIās carbon footprint.
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We work with clients on the infrastructure side to optimize workloads and make efficient use of data centres. We also work with clients to develop AI solutions that are industry-specific for sectors such as healthcare, finance, or manufacturing in order to meet ESG objectives.
What are the most efficient ways cloud solutions can reduce AIās environmental footprint?
: Cloud solutions support energy-efficient data centres by using renewables and optimizing cooling. They can also incorporate carbon-aware computing. AI model optimization can also be achieved through less energy-intensive methods such as federated training and model pruning.
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You may also align resources with demand using serverless and automatic-scaling software to avoid over-provisioning. Cloud providers offer dashboards for carbon tracking and reporting, allowing users to measure and optimize their footprint. Multi-cloud and edge computing can reduce data transfer and process AI closer at the source.
Leveraging cloud
How can cloud infrastructure help embed ethical considerations in AI development?
: Cloud infrastructure provides powerful tools to embed ethical considerations in AI development. AI ethics toolkits that are built-in can help detect bias and test fairness by identifying imbalances within training data and models. Cloud platforms offer diversity-aware tools to ensure datasets are inclusive and representative, which is crucial for developing responsible AI systems. Also:CTO vs. CMO AI Power Struggle – Who should be in charge?
Cloud-based AI frameworks offer explainability and transparent features to help you better understand how models take decisions. Through capabilities such as differential privacy and encrypted data processing, AI development can be made secure and privacy-preserving.
Cloud-based services can support ethical AI by automating compliance monitoring. This helps ensure adherence to regulations like GDPR and CCPA. There are also tools for model drift and hallucination testing, which make it easier to monitor model performance over time and flag inaccurate or unreliable results.
How can cloud-based tools assist in measuring AI’s impact on sustainability?
: Due to the lack of standard metrics, many organizations struggle to measure AIās sustainability impact. It is difficult to compare initiatives or benchmark progress without a framework that quantifies environmental effects. Cloud-based tools are able to bridge this gap, offering customizable dashboards and carbon output models across the AI lifecycle from development to deployment.
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Another challenge is real-time monitoring, as energy consumption for AI workloads can vary significantly. Static reporting methods miss these variations. Cloud platforms offer dynamic, real time tracking tools that adapt to shifting workloads. This allows for a more accurate view on energy usage.
In addition, fragmented visibility of data across cloud, on premises, and edge environments complicates assessments of sustainability. Cloud-native solutions aggregate data from different sources into a single view. This improves transparency and decision making.
Some AI’s environmental cost remain hidden. These costs go beyond training and include inference, storage and computing scaling. Cloud tools can reveal these less-known impacts through an analysis of end-to-end use patterns.
Compliance and regulatory gaps add to the complexity, particularly as ESG (environmental social and governance) reporting requirements differ by region. Cloud services can automate compliance tracking for each region.
Cloud-based analytics can help in navig ating the tradeoffs between model performance, cost, and sustainability. They offer insights that support a more balanced, responsible AI.
What concrete steps can be taken by organizations to improve AI transparency?
: First, train your workforce to use AI responsibly. Encourage your workforce to use AI in a safe environment by interrogating and querying it.
Nvidia’s efforts to prepare educators and students for the AI eraSecond, create a governance framework for AI that encompasses all aspects of business, including procurement, HR, CISO and risk management.
How does AI bias develop, and what role can cloud-based frameworks have in mitigating this?
: AI bias can be caused by a variety of sources, such as algorithmic training data which are unrepresentative, or which contain historical prejudices. It can also come from errors and inconsistencies within human-labeled datasets. AI decisions can be skewed by poor data if they are trained on it. This is because the data may not reflect cultural, corporate or societal ethical frameworks.
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Legacy AI systems based on outdated assumptions or historical data could continue to perpetuate biases. AI may also have difficulty with dialects, regional contexts or cultural nuances.
Cloud frameworks can mitigate this by monitoring compliance to diverse regional regulations, and ensuring fair AI models development through validation across diverse demographic, economic, and social groups. Cloud-based adaptive learning processes can also rebalance data sets to prevent power-dynamic biases.
What governance strategies can enterprises implement to ensure responsible AI use?
: The most important part is having a governance framework. Some organizations have a separate AI Governance structure, while others have integrated it into their existing governance construct.
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Involving all parts of the organization is important. AI impact assessments can be used to integrate legal protection, privacy and robustness into the deployment of AI at the earliest stages.
AI Issues
How do you feel about the increasing emphasis on ethical and sustainable AI in recent years? Has your organization adopted any frameworks or policies that ensure responsible AI development.
What are your plans for reducing the environmental impact of AI workloads? Do you use cloud-based tools that can help you measure this footprint or reduce it?
Do think global regulations keep up with AI innovation or are companies left to navigate gray areas on their lonesome? Comment below to let us know. Tech Today’s newsletter will deliver the top stories of the day to your inbox every morning.
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